William Jeynes
186 lines
4.5 KiB
Python
186 lines
4.5 KiB
Python
from transformers import RobertaTokenizer, RobertaForSequenceClassification, Trainer, TrainingArguments
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import torch
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
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from collections import Counter
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import sys
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import csv
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NUM_CLASSES = 2
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model_name = "roberta-base"
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LABEL_PRIORITY = [
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("PERFECT", 0),
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("STORY", 1),
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("NSPECIFIC", 1),
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("REWORDING", 1),
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("TINCORRECT", -1),
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("DUPLICATE", -1),
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("", 0), # fallback to PERFECT
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]
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def label_to_int(extra_info: str) -> int:
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"""
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Convert extra_info string to integer label using priority rules.
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"""
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if extra_info is None:
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extra_info = ""
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extra_info = extra_info.strip()
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# Handle empty string explicitly
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if extra_info == "":
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for key, value in LABEL_PRIORITY:
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if key == "":
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return value
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raise ValueError("Empty extra_info but no empty mapping defined")
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# Split words (case-insensitive)
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tokens = set(extra_info.upper().split())
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# Priority matching
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for key, value in LABEL_PRIORITY:
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if key == "":
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continue
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if key in tokens:
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return value
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raise ValueError(f"Unknown label content: '{extra_info}'")
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def load_dataset_from_csv(path):
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texts = []
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labels = []
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removed_rows = 0
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with open(path, newline="", encoding="utf-8") as f:
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reader = csv.DictReader(f)
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for i, row in enumerate(reader, start=1):
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text = row["event"]
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label_str = row["extra_info"]
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try:
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label_int = label_to_int(label_str)
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except Exception as e:
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print(f"ERROR converting label on line {i}: {label_str}")
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print(e)
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sys.exit(1)
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# Skip rows marked for removal
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if label_int == -1:
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removed_rows += 1
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continue
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texts.append(text)
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labels.append(label_int)
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print(f"Loaded {len(texts)} samples (removed {removed_rows})")
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return texts, labels
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def compute_metrics(eval_pred):
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logits, labels = eval_pred
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preds = logits.argmax(axis=1)
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return {
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"accuracy": accuracy_score(labels, preds),
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"f1": f1_score(labels, preds, average="weighted"),
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"precision": precision_score(labels, preds, average="weighted"),
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"recall": recall_score(labels, preds, average="weighted"),
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}
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texts, labels = load_dataset_from_csv("../../data/classify.csv")
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tokenizer = RobertaTokenizer.from_pretrained(model_name)
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model = RobertaForSequenceClassification.from_pretrained(
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model_name,
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num_labels=NUM_CLASSES
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)
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for param in model.roberta.parameters():
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param.requires_grad = False
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for param in model.roberta.encoder.layer[-2:].parameters():
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param.requires_grad = True
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print("Dataset size:", len(texts))
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print("Label distribution:")
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print(Counter(labels))
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train_texts, val_texts, train_labels, val_labels = train_test_split(
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texts,
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labels,
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test_size=0.2,
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random_state=42
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)
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train_encodings = tokenizer(
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train_texts,
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truncation=True,
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padding=True,
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max_length=256
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)
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val_encodings = tokenizer(
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val_texts,
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truncation=True,
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padding=True,
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max_length=256
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)
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class TextDataset(torch.utils.data.Dataset):
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def __init__(self, encodings, labels):
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self.encodings = encodings
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self.labels = labels
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def __getitem__(self, idx):
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item = {
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key: torch.tensor(val[idx])
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for key, val in self.encodings.items()
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}
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item["labels"] = torch.tensor(self.labels[idx])
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return item
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def __len__(self):
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return len(self.labels)
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training_args = TrainingArguments(
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output_dir="./results",
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learning_rate=1e-5,
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per_device_train_batch_size=8,
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num_train_epochs=15,
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weight_decay=0.01,
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load_best_model_at_end=True,
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eval_strategy="epoch",
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save_strategy="epoch",
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metric_for_best_model="f1",
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greater_is_better=True
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)
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train_dataset = TextDataset(train_encodings, train_labels)
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val_dataset = TextDataset(val_encodings, val_labels)
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset,
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eval_dataset=val_dataset,
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compute_metrics=compute_metrics
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)
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trainer.train()
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metrics = trainer.evaluate()
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print("Final evaluation metrics:")
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for k, v in metrics.items():
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print(f"{k}: {v}")
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trainer.save_model("./roberta_classifier")
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tokenizer.save_pretrained("./roberta_classifier") |